Cornac examples directory

Basic usage

first_example.py - Your very first example with Cornac.

pmf_ratio.py - Splitting data into train/val/test sets based on provided sizes (RatioSplit).

given_data.py - Evaluate the models with your own data splits.

propensity_stratified_evaluation_example.py - Evaluate the models with Propensity Stratified Evaluation method.

vbpr_tradesy.py - Image features associate with items/users.

c2pf_example.py - Items/users networks as graph modules.

conv_mf_example.py - Text data associate with items/users.

param_search.py - Hyper-parameter tuning with GridSearch and RandomSearch.

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Multimodal Algorithms (Using Auxiliary Data)

Graph

c2pf_example.py - Collaborative Context Poisson Factorization (C2PF) with Amazon Office dataset.

cvaecf_filmtrust.py - Fit and evaluate Conditional VAE (CVAECF) on the FilmTrust dataset.

mcf_office.py - Fit Matrix Co-Factorization (MCF) to the Amazon Office dataset.

pcrl_example.py - Probabilistic Collaborative Representation Learning (PCRL) Amazon Office dataset.

sbpr_epinions.py - Social Bayesian Personalized Ranking (SBPR) with Epinions dataset.

sorec_filmtrust.py - Social Recommendation using PMF (Sorec) with FilmTrust dataset.

Text

cdl_example.py - Collaborative Deep Learning (CDL) with CiteULike dataset.

cdr_example.py - Collaborative Deep Ranking (CDR) with CiteULike dataset.

conv_mf_example.py - Convolutional Matrix Factorization (ConvMF) with MovieLens dataset.

ctr_example_citeulike.py - Collaborative Topic Regression (CTR) with CiteULike dataset.

cvae_example.py - Collaborative Variational Autoencoder (CVAE) with CiteULike dataset.

efm_example.py - Explicit Factor Model (EFM) with Amazon Toy and Games dataset.

hft_example.py - Hidden Factor Topic (HFT) with MovieLen 1m dataset.

mter_example.py - Multi-Task Explainable Recommendation (MTER) with Amazon Toy and Games dataset.

Image

causalrec_clothing.py - CausalRec with Clothing dataset.

vbpr_tradesy.py - Visual Bayesian Personalized Ranking (VBPR) with Tradesy dataset.

vmf_clothing.py - Visual Matrix Factorization (VMF) with Amazon Clothing dataset.

Unimodal Algorithms

biased_mf.py - Matrix Factorization (MF) with biases.

bpr_netflix.py - Example to run Bayesian Personalized Ranking (BPR) with Netflix dataset.

ease_movielens.py - Embarrassingly Shallow Autoencoders (EASEᴿ) with MovieLens 1M dataset.

fm_example.py - Example to run Factorization Machines (FM) with MovieLens 100K dataset.

hpf_movielens.py - (Hierarchical) Poisson Factorization vs BPR on MovieLens data.

ibpr_example.py - Example to run Indexable Bayesian Personalized Ranking.

knn_movielens.py - Example to run Neighborhood-based models with MovieLens 100K dataset.

mmmf_exp.py - Maximum Margin Matrix Factorization (MMMF) with MovieLens 100K dataset.

ncf_example.py - Neural Collaborative Filtering (GMF, MLP, NeuMF) with Amazon Clothing dataset.

nmf_example.py - Non-negative Matrix Factorization (NMF) with RatioSplit.

pmf_ratio.py - Probabilistic Matrix Factorization (PMF) with RatioSplit.

skm_movielens.py - SKMeans vs BPR on MovieLens data.

svd_example.py - Singular Value Decomposition (SVD) with MovieLens dataset.

vaecf_citeulike.py - Variational Autoencoder for Collaborative Filtering (VAECF) with CiteULike dataset.

wmf_example.py - Weighted Matrix Factorization with CiteULike dataset.